Deep neural network-based approach for processing sequential data


Increasing volumes of offline/online data has thrown diverse challenges. Availability of the data has made the researchers to urge for new paradigms to analyze data to gain useful insights from it. Such analysis can be helpful for predictions in various domains such as medical informatics, cyber security, fraud detection etc. Further, problem of overloaded information is that it leads to over-choice, especially for users in web applications. Machine learning algorithms construct models that can learn from the data without being explicitly programmed, to perform predictions/decisions. However, the limitations of machine learning have paved a way to deep learning. Unlike the task specific algorithms of machine learning, deep learning learns feature representations from data itself. These traits enabled deep learning to achieve futuristic results for exceptionally complex tasks of various domains. Particularly in health care, natural language processing, information retrieval, computer vision etc. Area of research in this work focused on processing sequential data in the domains of ambient air quality management and recommender systems. Air quality data analysis is one of the domains where deep learning models have outperformed the traditional machine learning models. Hence, this research aims to build a model to predict ambient air quality using Amplified Recurrent Neural Networks for a particular geographical area. Recommender Systems (RS) have become a part of all the web applications these days. The main objective of RS is to asist users by avoiding information overload through generating personalized suggestions. Deep learning-based RS models have gained more prominence these days. Consequently, this research aims to develop deep learning-based recommenders/personal assistance/advisory systems to exploit the information systems and to capture its user’s preferences. The primary goals of the objectives were successfully accomplished with promising results.

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Fig. 1


  1. 1.

  2. 2.

  3. 3.

  4. 4.

  5. 5.

    Rao KS, Devi GL, Ramesh N (2019) Air quality prediction in Visakhapatnam with LSTM based recurrent neural networks. Int J Intell Syst Appl 10(2):18–24

    Google Scholar 

  6. 6.

    Kök İ, UlviŞimşek M, Özdemir S (2017) A deep learning model for air quality prediction in smart cities. In: Proceedings of IEEE international conference on big data (big data)

  7. 7.

    Devooght R, Kourtellis N, Mantrach A (2015) Dynamic matrix factorization with priors on unknown values. In: ACM KDD conference

  8. 8.

    Chen J, Zhang H, He X, Nie L, Liu W, Chua T-S (2017) Attentive collaborative filtering: multimedia recommendation with item- and component-level attention. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval

  9. 9.

    Georgiev K, Nakov P (2013) A non-IID framework for collaborative filtering with restricted boltzmann machines. In: International conference on machine learning, pp 1148–1156

  10. 10.

    Witten IH, Frank E (2002) Data mining: practical machine learning tools and techniques with Java implementations. ACM Sigmod Rec 31(1):76–77

    Article  Google Scholar 

  11. 11.

    Sujatha M, Devi GL, Rao KS, Ramesh N (2018) Rough set theory based missing value imputation. In: Cognitive science and health bioinformatics. Springer, Singapore, pp 97–106

  12. 12.

    Nelaturi N, Devi GL (2018) Hybrid recommender system leveraging stacked convolutional networks. J Eng Sci Technol Rev 11(3):89–96

    Article  Google Scholar 

  13. 13.

    Nelaturi N, Devi G (2019) A product recommendation model based on recurrent neural network. J Eur Syst Autom 52:501–507

    Google Scholar 

  14. 14.

    RamaKrishna TVBPS, Reddy MK, Reddy RC, Singh RN, Devotta S (2006) Modelling of ambient air quality over Visakhapatnam bowl area. Proc Indian Natl Sci Acad 72(1):55–61

    Google Scholar 

  15. 15.

  16. 16.

    Reddy VN, Mohanty S (2017) Deep air: forecasting air pollution in Beijing, China.

  17. 17.

    Srinivasa Rao S, Srinivasa Rajamani N, Reddi EUB (2015) Dispersal conditions and assimilative capacity of air environment at Gajuwaka industrial hub in Visakhapatnam. Int J Sci Res Sci Eng Technol 1(4)

  18. 18.

    Li X, Peng L, Yao X, Cui S, Hu Y, You C, Chi T (2017) Long short-term memory neural network for air pollutant concentration predictions: method development and evaluation. Environ Pollut 231:997–1004

    Article  Google Scholar 

  19. 19.

    Neelapu R, Devi GL, Rao KS (2018) Deep learning based conventional neural network architecture for medical image classification. J Trait Signal 35:169–182

    Article  Google Scholar 

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Correspondence to Lavanya Devi Golagani.

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Golagani, L.D., Nelaturi, N. & Kurapati, S.R. Deep neural network-based approach for processing sequential data. CSIT 8, 263–270 (2020).

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  • Sequence modelling
  • Recurrent neural network
  • Deep learning
  • Recommender system
  • Air ambience analysis